Learning robot policies using a high-level abstraction persona-behaviour simulator

2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other worksCollecting data in Human-Robot Interaction for training learning agents might be a hard task to accomplish. This is especially true when the target users are older adults with dementia since this usually requires hours of interactions and puts quite a lot of workload on the user. This paper addresses the problem of importing the Personas technique from HRI to create fictional patients’ profiles. We propose a Persona-Behaviour Simulator tool that provides, with high-level abstraction, user’s actions during an HRI task, and we apply it to cognitive training exercises for older adults with dementia. It consists of a Persona Definition that characterizes a patient along four dimensions and a Task Engine that provides information regarding the task complexity. We build a simulated environment where the high-level user’s actions are provided by the simulator and the robot initial policy is learned using a Q-learning algorithm. The results show that the current simulator provides a reasonable initial policy for a defined Persona profile. Moreover, the learned robot assistance has proved to be robust to potential changes in the user’s behaviour. In this way, we can speed up the fine-tuning of the rough policy during the real interactions to tailor the assistance to the given user. We believe the presented approach can be easily extended to account for other types of HRI tasks; for example, when input data is required to train a learning algorithm, but data collection is very expensive or unfeasible. We advocate that simulation is a convenient tool in these cases.Peer ReviewedPostprint (author's final draft

Introducing CARESSER: A framework for in situ learning robot social assistance from expert knowledge and demonstrations

Socially assistive robots have the potential to augment and enhance therapist’s effectiveness in repetitive tasks such as cognitive therapies. However, their contribution has generally been limited as domain experts have not been fully involved in the entire pipeline of the design process as well as in the automatisation of the robots’ behaviour. In this article, we present aCtive leARning agEnt aSsiStive bEhaviouR (CARESSER), a novel framework that actively learns robotic assistive behaviour by leveraging the therapist’s expertise (knowledge-driven approach) and their demonstrations (data-driven approach). By exploiting that hybrid approach, the presented method enables in situ fast learning, in a fully autonomous fashion, of personalised patient-specific policies. With the purpose of evaluating our framework, we conducted two user studies in a daily care centre in which older adults affected by mild dementia and mild cognitive impairment (N = 22) were requested to solve cognitive exercises with the support of a therapist and later on of a robot endowed with CARESSER. Results showed that: (i) the robot managed to keep the patients’ performance stable during the sessions even more so than the therapist; (ii) the assistance offered by the robot during the sessions eventually matched the therapist’s preferences. We conclude that CARESSER, with its stakeholder-centric design, can pave the way to new AI approaches that learn by leveraging human–human interactions along with human expertise, which has the benefits of speeding up the learning process, eliminating the need for the design of complex reward functions, and finally avoiding undesired states.Peer ReviewedPostprint (published version

Multi-Agent Systems

This Special Issue ""Multi-Agent Systems"" gathers original research articles reporting results on the steadily growing area of agent-oriented computing and multi-agent systems technologies. After more than 20 years of academic research on multi-agent systems (MASs), in fact, agent-oriented models and technologies have been promoted as the most suitable candidates for the design and development of distributed and intelligent applications in complex and dynamic environments. With respect to both their quality and range, the papers in this Special Issue already represent a meaningful sample of the most recent advancements in the field of agent-oriented models and technologies. In particular, the 17 contributions cover agent-based modeling and simulation, situated multi-agent systems, socio-technical multi-agent systems, and semantic technologies applied to multi-agent systems. In fact, it is surprising to witness how such a limited portion of MAS research already highlights the most relevant usage of agent-based models and technologies, as well as their most appreciated characteristics. We are thus confident that the readers of Applied Sciences will be able to appreciate the growing role that MASs will play in the design and development of the next generation of complex intelligent systems. This Special Issue has been converted into a yearly series, for which a new call for papers is already available at the Applied Sciences journal’s website: https://www.mdpi.com/journal/applsci/special_issues/Multi-Agent_Systems_2019

What makes a social robot good at interacting with humans?

This paper discusses the nuances of a social robot, how and why social robots are becoming increasingly significant, and what they are currently being used for. This paper also reflects on the current design of social robots as a means of interaction with humans and also reports potential solutions about several important questions around the futuristic design of these robots. The specific questions explored in this paper are: “Do social robots need to look like living creatures that already exist in the world for humans to interact well with them?”; “Do social robots need to have animated faces for humans to interact well with them?”; “Do social robots need to have the ability to speak a coherent human language for humans to interact well with them?” and “Do social robots need to have the capability to make physical gestures for humans to interact well with them?”. This paper reviews both verbal as well as nonverbal social and conversational cues that could be incorporated into the design of social robots, and also briefly discusses the emotional bonds that may be built between humans and robots. Facets surrounding acceptance of social robots by humans and also ethical/moral concerns have also been discussed

What makes a social robot good at interacting with humans?

This paper discusses the nuances of a social robot, how and why social robots are becoming increasingly significant, and what they are currently being used for. This paper also reflects on the current design of social robots as a means of interaction with humans and also reports potential solutions about several important questions around the futuristic design of these robots. The specific questions explored in this paper are: “Do social robots need to look like living creatures that already exist in the world for humans to interact well with them?”; “Do social robots need to have animated faces for humans to interact well with them?”; “Do social robots need to have the ability to speak a coherent human language for humans to interact well with them?” and “Do social robots need to have the capability to make physical gestures for humans to interact well with them?”. This paper reviews both verbal as well as nonverbal social and conversational cues that could be incorporated into the design of social robots, and also briefly discusses the emotional bonds that may be built between humans and robots. Facets surrounding acceptance of social robots by humans and also ethical/moral concerns have also been discussed

Simulation Models in Education

This paper introduces the use of simulation models in an e-learning environment. Nowadays, simulation models are a part of computer-assisted learning and thus an important guide for lifelong education. E-learning systems, combined with the use of simulation models as tool for interactivity, are the best way to provide some kind of virtual reality in education. Important techniques for building a usable simulation model are also presented. A good model has to be accompanied by texts, demonstration material, worksheets, teachers' guides, student manuals, as well as tools for the teacher in order to be able to make changes in the computer simulation program, like a text-editor, a graphic editor as well as a resource editor. In addition, the paper describes why it is important to follow the building scheme and discusses the problem of credibility. Then, these models are explained as a method of learning dependent on and independent from the use of computers and a view of its valuable aspects is shown. After presenting various examples from the primary and secondary schools to the university and in lifelong learning, examples of their use in information sciences have been presented, as well as need to include the course of building simulation model in the curriculum

Proceedings of The Multi-Agent Logics, Languages, and Organisations Federated Workshops (MALLOW 2010)

http://ceur-ws.org/Vol-627/allproceedings.pdfInternational audienceMALLOW-2010 is a third edition of a series initiated in 2007 in Durham, and pursued in 2009 in Turin. The objective, as initially stated, is to "provide a venue where: the cost of participation was minimum; participants were able to attend various workshops, so fostering collaboration and cross-fertilization; there was a friendly atmosphere and plenty of time for networking, by maximizing the time participants spent together"

Representation learning for dialogue systems

Cette thèse présente une série de mesures prises pour étudier l’apprentissage de représentations (par exemple, l’apprentissage profond) afin de mettre en place des systèmes de dialogue et des agents de conversation virtuels. La thèse est divisée en deux parties générales. La première partie de la thèse examine l’apprentissage des représentations pour les modèles de dialogue génératifs. Conditionnés sur une séquence de tours à partir d’un dialogue textuel, ces modèles ont la tâche de générer la prochaine réponse appropriée dans le dialogue. Cette partie de la thèse porte sur les modèles séquence-à-séquence, qui est une classe de réseaux de neurones profonds génératifs. Premièrement, nous proposons un modèle d’encodeur-décodeur récurrent hiérarchique ("Hierarchical Recurrent Encoder-Decoder"), qui est une extension du modèle séquence-à-séquence traditionnel incorporant la structure des tours de dialogue. Deuxièmement, nous proposons un modèle de réseau de neurones récurrents multi-résolution ("Multiresolution Recurrent Neural Network"), qui est un modèle empilé séquence-à-séquence avec une représentation stochastique intermédiaire (une "représentation grossière") capturant le contenu sémantique abstrait communiqué entre les locuteurs. Troisièmement, nous proposons le modèle d’encodeur-décodeur récurrent avec variables latentes ("Latent Variable Recurrent Encoder-Decoder"), qui suivent une distribution normale. Les variables latentes sont destinées à la modélisation de l’ambiguïté et l’incertitude qui apparaissent naturellement dans la communication humaine. Les trois modèles sont évalués et comparés sur deux tâches de génération de réponse de dialogue: une tâche de génération de réponses sur la plateforme Twitter et une tâche de génération de réponses de l’assistance technique ("Ubuntu technical response generation task"). La deuxième partie de la thèse étudie l’apprentissage de représentations pour un système de dialogue utilisant l’apprentissage par renforcement dans un contexte réel. Cette partie porte plus particulièrement sur le système "Milabot" construit par l’Institut québécois d’intelligence artificielle (Mila) pour le concours "Amazon Alexa Prize 2017". Le Milabot est un système capable de bavarder avec des humains sur des sujets populaires à la fois par la parole et par le texte. Le système consiste d’un ensemble de modèles de récupération et de génération en langage naturel, comprenant des modèles basés sur des références, des modèles de sac de mots et des variantes des modèles décrits ci-dessus. Cette partie de la thèse se concentre sur la tâche de sélection de réponse. À partir d’une séquence de tours de dialogues et d’un ensemble des réponses possibles, le système doit sélectionner une réponse appropriée à fournir à l’utilisateur. Une approche d’apprentissage par renforcement basée sur un modèle appelée "Bottleneck Simulator" est proposée pour sélectionner le candidat approprié pour la réponse. Le "Bottleneck Simulator" apprend un modèle approximatif de l’environnement en se basant sur les trajectoires de dialogue observées et le "crowdsourcing", tout en utilisant un état abstrait représentant la sémantique du discours. Le modèle d’environnement est ensuite utilisé pour apprendre une stratégie d’apprentissage du renforcement par le biais de simulations. La stratégie apprise a été évaluée et comparée à des approches concurrentes via des tests A / B avec des utilisateurs réel, où elle démontre d’excellente performance.This thesis presents a series of steps taken towards investigating representation learning (e.g. deep learning) for building dialogue systems and conversational agents. The thesis is split into two general parts. The first part of the thesis investigates representation learning for generative dialogue models. Conditioned on a sequence of turns from a text-based dialogue, these models are tasked with generating the next, appropriate response in the dialogue. This part of the thesis focuses on sequence-to-sequence models, a class of generative deep neural networks. First, we propose the Hierarchical Recurrent Encoder-Decoder model, which is an extension of the vanilla sequence-to sequence model incorporating the turn-taking structure of dialogues. Second, we propose the Multiresolution Recurrent Neural Network model, which is a stacked sequence-to-sequence model with an intermediate, stochastic representation (a "coarse representation") capturing the abstract semantic content communicated between the dialogue speakers. Third, we propose the Latent Variable Recurrent Encoder-Decoder model, which is a variant of the Hierarchical Recurrent Encoder-Decoder model with latent, stochastic normally-distributed variables. The latent, stochastic variables are intended for modelling the ambiguity and uncertainty occurring naturally in human language communication. The three models are evaluated and compared on two dialogue response generation tasks: a Twitter response generation task and the Ubuntu technical response generation task. The second part of the thesis investigates representation learning for a real-world reinforcement learning dialogue system. Specifically, this part focuses on the Milabot system built by the Quebec Artificial Intelligence Institute (Mila) for the Amazon Alexa Prize 2017 competition. Milabot is a system capable of conversing with humans on popular small talk topics through both speech and text. The system consists of an ensemble of natural language retrieval and generation models, including template-based models, bag-of-words models, and variants of the models discussed in the first part of the thesis. This part of the thesis focuses on the response selection task. Given a sequence of turns from a dialogue and a set of candidate responses, the system must select an appropriate response to give the user. A model-based reinforcement learning approach, called the Bottleneck Simulator, is proposed for selecting the appropriate candidate response. The Bottleneck Simulator learns an approximate model of the environment based on observed dialogue trajectories and human crowdsourcing, while utilizing an abstract (bottleneck) state representing high-level discourse semantics. The learned environment model is then employed to learn a reinforcement learning policy through rollout simulations. The learned policy has been evaluated and compared to competing approaches through A/B testing with real-world users, where it was found to yield excellent performance

A serious game to improve engagement with web accessibility guidelines

This is an Accepted Manuscript of an article published by Taylor & Francis in Behaviour & Information Technology on 07 Jan 2020, available online: https://www.tandfonline.com/doi/full/10.1080/0144929X.2019.171145

Artificial general intelligence: Proceedings of the Second Conference on Artificial General Intelligence, AGI 2009, Arlington, Virginia, USA, March 6-9, 2009

Artificial General Intelligence (AGI) research focuses on the original and ultimate goal of AI – to create broad human-like and transhuman intelligence, by exploring all available paths, including theoretical and experimental computer science, cognitive science, neuroscience, and innovative interdisciplinary methodologies. Due to the difficulty of this task, for the last few decades the majority of AI researchers have focused on what has been called narrow AI – the production of AI systems displaying intelligence regarding specific, highly constrained tasks. In recent years, however, more and more researchers have recognized the necessity – and feasibility – of returning to the original goals of the field. Increasingly, there is a call for a transition back to confronting the more difficult issues of human level intelligence and more broadly artificial general intelligence